XAI Builder

Purpose & User Intent: XAI Builder solves the need to design and validate explainable AI models. It targets researchers, ML engineers, risk managers, and governance professionals who require interpretable model behavior during development, auditing, and regulatory reviews. The tool provides attribution generation, global explanations, and cross-model comparison to establish transparency.
Core Logic & Features: Required features include a trained model input, a representative dataset input, and an attribution method selection. Optional/advanced features include scenario-based explanations, batch processing, multi-model comparisons, and exportable, audit-ready reports. The system enforces data validation, supports common model formats, and computes explainability metrics to guide governance decisions.
Inputs & Outputs: Inputs include dataset (CSV), model (file or API reference), explainability method (SHAP, Integrated Gradients, LIME), and run configuration (precision, sampling). Outputs include per-feature attributions, global explanations, fidelity and stability metrics, and a structured report suitable for regulators and stakeholders.
Algorithms & Calculations: The tool applies attribution algorithms to generate local explanations, aggregates them into global explanations, and computes fidelity (surrogate-model checks) and stability (perturbation tests). It supports cross-model aggregation and scoring to compare interpretability across approaches.
Error & Edge Cases: Handles missing inputs, unsupported model formats, and insufficient data with explicit validation. When data quality is poor, it emits conservative attribution estimates and records warnings in logs. External data unavailability or API failures are surfaced as error codes and non-fatal alerts, preserving existing results.
Industry/Region & Localization: Aligns with AI governance norms used in US/EU contexts; supports regional reporting formats, currency/time-zone reporting in exports, and language-agnostic attributions suitable for compliance reviews. Assumptions & Exclusions: Assumes access to a trained model and representative data; does not perform model training or labeling. Excludes UI styling, front-end behavior, and any non-analytic visualization rendering beyond explainability outputs.